{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-11-08T11:39:23.998019300Z",
     "start_time": "2023-11-08T11:38:52.671533300Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torchvision import transforms,datasets\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "data_path = 'F:/图像识别数据集/archive'\n",
    "import os\n",
    "data_transforms = {\n",
    "    'train':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ]),\n",
    "    'test':transforms.Compose([\n",
    "        transforms.Resize(230),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "    ])\n",
    "}\n",
    "trainset = datasets.ImageFolder(os.path.join(data_path,'train'),data_transforms['train'])\n",
    "testset = datasets.ImageFolder(os.path.join(data_path,'val'),data_transforms['test'])\n",
    "def imshow(inputs):\n",
    "    inputs = inputs/2+0.5\n",
    "    inputs = inputs.numpy().transpose(1,2,0)\n",
    "    plt.imshow(inputs)\n",
    "    plt.show()\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(trainset,batch_size=5,shuffle=True,num_workers=4)\n",
    "test_loader = torch.utils.data.DataLoader(testset,batch_size=5,shuffle=True,num_workers=4)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T11:59:12.243430500Z",
     "start_time": "2023-11-08T11:59:12.209435900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dog\n",
      "dog\n",
      "dog\n",
      "cat\n",
      "dog\n"
     ]
    }
   ],
   "source": [
    "inputs,labels=next(iter(test_loader))\n",
    "class_names =['cat','dog']\n",
    "imshow(torchvision.utils.make_grid(inputs))\n",
    "for i in labels:\n",
    "    print(class_names[i])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T13:14:49.662015500Z",
     "start_time": "2023-11-08T13:14:43.880406100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## alexnet模型"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlexNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): ReLU(inplace=True)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Dropout(p=0.5, inplace=False)\n",
      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "alexnet = torchvision.models.alexnet(pretrained=True)\n",
    "print(alexnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:02:02.564011300Z",
     "start_time": "2023-11-08T12:02:00.977722Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "for p in alexnet.parameters():\n",
    "    p.requires_grad=False\n",
    "\n",
    "alexnet.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(9216,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Linear(4096,2,bias=True)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:05:47.723642300Z",
     "start_time": "2023-11-08T12:05:47.342639400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "def train_loop(model,optimizer,loss_fn,n_epochs,train_loader):\n",
    "    for epoch in range(1,n_epochs+1):\n",
    "        loss_train = 0.0\n",
    "        for i,data in enumerate(train_loader,1):\n",
    "            imgs,labels = data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            loss = loss_fn(outputs,labels)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            loss_train+=loss.item()\n",
    "            if i%10==0:\n",
    "                print('Epoch:{}, i:{}, 训练损失:{}'.format(epoch,i,loss_train/len(train_loader)))\n",
    "                loss_train=0.0"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:24:32.215518Z",
     "start_time": "2023-11-08T12:24:32.211495Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.05556560629470782\n",
      "Epoch:1, i:20, 训练损失:0.03439483339398893\n",
      "Epoch:1, i:30, 训练损失:0.055012439987198875\n",
      "Epoch:1, i:40, 训练损失:0.049517299979925156\n",
      "Epoch:1, i:50, 训练损失:0.012654793705769407\n",
      "Epoch:2, i:10, 训练损失:0.039006161361149594\n",
      "Epoch:2, i:20, 训练损失:0.020861057938732715\n",
      "Epoch:2, i:30, 训练损失:0.04653715332123366\n",
      "Epoch:2, i:40, 训练损失:0.019490996707992796\n",
      "Epoch:2, i:50, 训练损失:0.03992991508408026\n"
     ]
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(alexnet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=alexnet.cuda(),loss_fn=loss_fn,optimizer=optimizer,train_loader=train_loader,n_epochs=2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:24:48.478253800Z",
     "start_time": "2023-11-08T12:24:34.002346600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "def test_loop(model,test_loader):\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in test_loader:\n",
    "            imgs,labels = data\n",
    "            imgs,labels = imgs.cuda(),labels.cuda()\n",
    "            outputs = model(imgs)\n",
    "            _,preds = torch.max(outputs,dim=1)\n",
    "            correct+=int((preds==labels).sum())\n",
    "            total+=labels.shape[0]\n",
    "    print('测试集精度:{:3f}%'.format(correct/total*100))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:34:16.357719Z",
     "start_time": "2023-11-08T12:34:16.353699600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:95.714286%\n"
     ]
    }
   ],
   "source": [
    "test_loop(alexnet.cuda().eval(),test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:34:23.568619600Z",
     "start_time": "2023-11-08T12:34:16.984042200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## vgg网络"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "vgg = torchvision.models.vgg16(pretrained=True)\n",
    "print(vgg)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:39:17.380963700Z",
     "start_time": "2023-11-08T12:39:14.710338200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "for p in vgg.parameters():\n",
    "    p.requires_grad = False\n",
    "\n",
    "vgg.classifier = nn.Sequential(\n",
    "    nn.Linear(25088,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,4096,bias=True),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Linear(4096,2,bias=True)\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:44:07.449119100Z",
     "start_time": "2023-11-08T12:44:06.568536400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.11236600225622004\n",
      "Epoch:1, i:20, 训练损失:0.05924926616928794\n",
      "Epoch:1, i:30, 训练损失:0.03199073353951627\n",
      "Epoch:1, i:40, 训练损失:0.04160148657181046\n",
      "Epoch:1, i:50, 训练损失:0.02824962115423246\n",
      "Epoch:2, i:10, 训练损失:0.006056782071986659\n",
      "Epoch:2, i:20, 训练损失:0.007396647977558049\n",
      "Epoch:2, i:30, 训练损失:0.0019259220264344053\n",
      "Epoch:2, i:40, 训练损失:0.0016973314272366803\n",
      "Epoch:2, i:50, 训练损失:0.005611207434875806\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(vgg.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=vgg.cuda(),optimizer=optimizer,loss_fn=loss_fn,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:48:28.052136800Z",
     "start_time": "2023-11-08T12:47:57.222971400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:98.571429%\n"
     ]
    }
   ],
   "source": [
    "test_loop(model=vgg.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:50:33.549470Z",
     "start_time": "2023-11-08T12:50:26.712841600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## resnet"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet101_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet101_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (6): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (9): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (13): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (14): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (15): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (16): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (17): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (18): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (19): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (20): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (21): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (22): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "resnet = torchvision.models.resnet101(pretrained=True)\n",
    "print(resnet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:51:59.252584900Z",
     "start_time": "2023-11-08T12:51:56.997455600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "for p in resnet.parameters():\n",
    "    p.requires_grad=False\n",
    "\n",
    "resnet.fc = nn.Linear(2048,2,bias=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:54:04.879226Z",
     "start_time": "2023-11-08T12:54:04.855205900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.10934117815711282\n",
      "Epoch:1, i:20, 训练损失:0.06298827854069797\n",
      "Epoch:1, i:30, 训练损失:0.06697920533743772\n",
      "Epoch:1, i:40, 训练损失:0.044193721494891425\n",
      "Epoch:1, i:50, 训练损失:0.06510972861539234\n",
      "Epoch:2, i:10, 训练损失:0.05364765419878743\n",
      "Epoch:2, i:20, 训练损失:0.02285302628509023\n",
      "Epoch:2, i:30, 训练损失:0.015023622424765067\n",
      "Epoch:2, i:40, 训练损失:0.030431448871439153\n",
      "Epoch:2, i:50, 训练损失:0.054239911450581116\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(resnet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=resnet.cuda(),loss_fn=loss_fn,optimizer=optimizer,n_epochs=2,train_loader=train_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:55:43.826024600Z",
     "start_time": "2023-11-08T12:55:17.940084700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:98.571429%\n"
     ]
    }
   ],
   "source": [
    "test_loop(model=resnet.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T12:57:11.930376300Z",
     "start_time": "2023-11-08T12:57:05.563373Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## squeezenet"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SqueezeNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (3): Fire(\n",
      "      (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Fire(\n",
      "      (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (6): Fire(\n",
      "      (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (7): Fire(\n",
      "      (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
      "    (9): Fire(\n",
      "      (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (10): Fire(\n",
      "      (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (11): Fire(\n",
      "      (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "    (12): Fire(\n",
      "      (squeeze): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (squeeze_activation): ReLU(inplace=True)\n",
      "      (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "      (expand1x1_activation): ReLU(inplace=True)\n",
      "      (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "      (expand3x3_activation): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Conv2d(512, 1000, kernel_size=(1, 1), stride=(1, 1))\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "E:\\myAnaconda\\envs\\PyTorch\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=SqueezeNet1_1_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_1_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "squeezenet = torchvision.models.squeezenet1_1(pretrained=True)\n",
    "print(squeezenet)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T13:08:09.630036600Z",
     "start_time": "2023-11-08T13:08:09.580357700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "for p in squeezenet.parameters():\n",
    "    p.requires_grad = False\n",
    "\n",
    "squeezenet.classifier = nn.Sequential(\n",
    "    nn.Dropout(p=0.5,inplace=False),\n",
    "    nn.Conv2d(512,2,kernel_size=(1,1),stride=(1,1)),\n",
    "    nn.ReLU(inplace=True),\n",
    "    nn.AdaptiveAvgPool2d(output_size=(1,1))\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T13:08:23.028286300Z",
     "start_time": "2023-11-08T13:08:23.008295400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:1, i:10, 训练损失:0.14448533701625738\n",
      "Epoch:1, i:20, 训练损失:0.09083342606371099\n",
      "Epoch:1, i:30, 训练损失:0.06092406538399783\n",
      "Epoch:1, i:40, 训练损失:0.081795165755532\n",
      "Epoch:1, i:50, 训练损失:0.0583886829289523\n",
      "Epoch:2, i:10, 训练损失:0.025658962604674426\n",
      "Epoch:2, i:20, 训练损失:0.024355379911139608\n",
      "Epoch:2, i:30, 训练损失:0.035478311641649767\n",
      "Epoch:2, i:40, 训练损失:0.024048299884254283\n",
      "Epoch:2, i:50, 训练损失:0.03582909963601692\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(squeezenet.parameters(),lr=0.001,momentum=0.9)\n",
    "train_loop(model=squeezenet.cuda(),loss_fn=loss_fn,optimizer=optimizer,train_loader=train_loader,n_epochs=2)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T13:08:36.353676500Z",
     "start_time": "2023-11-08T13:08:23.878708200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度:88.571429%\n"
     ]
    }
   ],
   "source": [
    "test_loop(model=squeezenet.cuda().eval(),test_loader=test_loader)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T13:08:44.743210800Z",
     "start_time": "2023-11-08T13:08:38.977223400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 做预测"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "真实值---预测值\n",
      "dog --- dog\n",
      "dog --- dog\n",
      "dog --- dog\n",
      "cat --- cat\n",
      "dog --- dog\n"
     ]
    }
   ],
   "source": [
    "models = vgg.cuda().eval()\n",
    "outputs=models(inputs.cuda())\n",
    "_,preds = torch.max(outputs,dim=1)\n",
    "print('真实值---预测值')\n",
    "for i,j in zip(labels,preds):\n",
    "    print(class_names[i],'---',class_names[j])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T13:15:01.650785Z",
     "start_time": "2023-11-08T13:15:01.301786100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(torchvision.utils.make_grid(inputs))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-08T13:15:04.484668800Z",
     "start_time": "2023-11-08T13:15:04.253676300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
